Fixes issue #12233: Avoid log(0) in KL, ruff and build checks

machine_learning/ridge_regression/model.py

@@ -1,56 +1,66 @@
 import numpy as np
 import pandas as pd
 
+
 class RidgeRegression:
     def __init__(self, alpha:float=0.001, regularization_param:float=0.1, num_iterations:int=1000) -> None:
         self.alpha:float = alpha
         self.regularization_param:float = regularization_param
         self.num_iterations:int = num_iterations
         self.theta:np.ndarray = None
 
+<<<<<<< HEAD
 
     def feature_scaling(self, X:np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+=======
+    def feature_scaling(self, X):
+>>>>>>> d4fc2bf852ec4a023380f4ef367edefa88fd6881
         mean = np.mean(X, axis=0)
         std = np.std(X, axis=0)
-        
+
         # avoid division by zero for constant features (std = 0)
         std[std == 0] = 1  # set std=1 for constant features to avoid NaN
-        
+
         X_scaled = (X - mean) / std
         return X_scaled, mean, std
-
 
     def fit(self, X:np.ndarray, y:np.ndarray) -> None:
         X_scaled, mean, std = self.feature_scaling(X)
         m, n = X_scaled.shape
         self.theta = np.zeros(n)  # initializing weights to zeros
-        
+
         for i in range(self.num_iterations):
             predictions = X_scaled.dot(self.theta)
             error = predictions - y
-            
+
             # computing gradient with L2 regularization
-            gradient = (X_scaled.T.dot(error) + self.regularization_param * self.theta) / m
+            gradient = (
+                X_scaled.T.dot(error) + self.regularization_param * self.theta
+            ) / m
             self.theta -= self.alpha * gradient  # updating weights
 
-
     def predict(self, X:np.ndarray) -> np.ndarray:
         X_scaled, _, _ = self.feature_scaling(X)
         return X_scaled.dot(self.theta)
-
 
+<<<<<<< HEAD
     def compute_cost(self, X:np.ndarray, y:np.ndarray) -> float:
         X_scaled, _, _ = self.feature_scaling(X)  
+=======
+    def compute_cost(self, X, y):
+        X_scaled, _, _ = self.feature_scaling(X)
+>>>>>>> d4fc2bf852ec4a023380f4ef367edefa88fd6881
         m = len(y)
-        
+
         predictions = X_scaled.dot(self.theta)
-        cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (self.regularization_param / (2 * m)) * np.sum(self.theta**2)
+        cost = (1 / (2 * m)) * np.sum((predictions - y) ** 2) + (
+            self.regularization_param / (2 * m)
+        ) * np.sum(self.theta**2)
         return cost
-
 
     def mean_absolute_error(self, y_true:np.ndarray, y_pred:np.ndarray) -> float:
         return np.mean(np.abs(y_true - y_pred))
-    
+
 
 # Example usage
 if __name__ == "__main__":
@@ -59,8 +69,13 @@
     y = df["ADR"].values
     y = (y - np.mean(y)) / np.std(y)
 
+<<<<<<< HEAD
     # added bias term to the feature matrix
     X = np.c_[np.ones(X.shape[0]), X] 
+=======
+    # Add bias term (intercept) to the feature matrix
+    X = np.c_[np.ones(X.shape[0]), X]
+>>>>>>> d4fc2bf852ec4a023380f4ef367edefa88fd6881
 
     # initialize and train the ridge regression model
     model = RidgeRegression(alpha=0.01, regularization_param=0.1, num_iterations=1000)
@@ -72,4 +87,4 @@
     # results
     print("Optimized Weights:", model.theta)
     print("Cost:", model.compute_cost(X, y))
-    print("Mean Absolute Error:", model.mean_absolute_error(y, predictions))
+    print("Mean Absolute Error:", model.mean_absolute_error(y, predictions))